Match analysis and probability of winning a point in elite men’s singles tennis

Iván Prieto-Lage; Adrián Paramés-González; Daniel Torres-Santos; Juan Carlos Argibay-González; Xoana Reguera-López-de-la-Osa; Alfonso Gutiérrez-Santiago

doi:10.1371/journal.pone.0286076

. 2023 Sep 28;18(9):e0286076. doi: 10.1371/journal.pone.0286076

Match analysis and probability of winning a point in elite men’s singles tennis

Iván Prieto-Lage ^1,^*, Adrián Paramés-González ¹, Daniel Torres-Santos ¹, Juan Carlos Argibay-González ¹, Xoana Reguera-López-de-la-Osa ², Alfonso Gutiérrez-Santiago ¹

Editor: Javier Abián-Vicén³

PMCID: PMC10538650 PMID: 37768928

Abstract

Notational analysis and new technologies have allowed a better understanding of tactical actions in tennis. In particular, the combined analysis of different variables affecting performance is necessary to understand the relationships between actions in competition. The aim of this research was to analyse the probability of winning a point in men’s professional tennis based on the most relevant variables affecting performance in this sport. A total of 4,669 points were analysed on three different court surfaces from the final rounds (from the quarter-finals onwards) of three of the four Grand Slam tournaments in the 2021 season. An observational methodology was applied. Different analysis techniques were used to obtain the results: descriptive and chi-square with a significance level of p<0.05. First serve effectiveness (point won) was 69% on clay, 75% on grass and 75% on hard court. Second serve effectiveness (point won) was around 55% regardless of the surface. The majority of points, between 65% and 77% depending on the court surface, ended with a short rally (between one and four shots). Approximately 80% of the points played with first serve and short rally were won by the serving player. With first serve and medium length rallies, the probability of winning the point is similar between server (range 49–55%) and receiver on any court surface. The study reveals a set of patterns (based on the combination of information from the variables analysed) that determine the probability of winning a point. Descriptive data from this research could help coaches and players on match strategy at the highest levels of elite men’s single tennis.

Introduction

Match analysis in tennis is a topic of interest to sport performance researchers, as can be seen in the numerous investigations that have proliferated recently [1–4]. Like many sports, tennis has a long history of qualitative and quantitative performance analysis [5], most of it predating the automated data collection systems recently seen in modern sport. The automation of data collection and reporting systems has recently accelerated due to new analytical tools and a growing awareness in sport/coaching of the value of analytics [1,5].

The introduction of new technologies in this sport is generating a large amount of data when analysing the competition. It is now possible to access statistical information of different types without difficulty. The Association of Tennis Professionals (ATP), the Women’s Tennis Association (WTA) and the International Tennis Federation (ITF) provide statistics on the most relevant competitions through their partnerships with Infosys, SAP and Slam Tracker [6]. This data can help tennis players’ teams focus on the search for meaningful variables for performance enhancement. It can also be useful to prepare match strategies and help decision making during the match [5,7]. New analytical techniques have improved the interpretation of sports data and provided more meaningful insights [8]. This data analysis provides insight into the variables that have the greatest influence on the final result of the match [9].

The analysis of the match has allowed the extraction of information that affects the performance related to the type of game [10,11], physical condition [12], technical [13], tactical [2] and psychological [14] aspects. Most researchers agree that the serve is the most decisive technical action of the match [3,15], due to the fact that the server has the first opportunity to win the point through an ace, an error by the opponent or to obtain a tactical superiority that puts them in an advantageous situation during the first shots [16], where it has been shown that most of the points are finished [17]. The first serve (putting the ball into play) barely varies according to the surface (between 62–64% depending on the court surface), although differences have been found in terms of achieving a direct serve (ace) [3]. Numerous studies have shown a clear difference in the number of points won depending on whether the first or second serve is made. While it is true that the player wins 69–75% of the points on the first serve, the percentage drops to 47–57% on the second serve, depending on the surface [3,13,18,19].

Another key factor is the type of surface on which the match is played [20]. The ITF classification divides court surfaces into 5 categories (slow, medium slow, medium, medium fast and fast). In the case of slow surfaces (e.g.: clay), the ball will present a higher coefficient of friction, a decreased horizontal speed and an increased bounce height; in the case of fast surfaces (e.g.: grass or hard track) the opposite will be the case [21]. In each tournament the court surface is different [22] and even when matches are played on natural surfaces (grass and clay), it must be taken into account that the surface varies as the days of competition progress, which can cause points to be played faster or slower [23] (without considering weather factors as well). According to the experts, the type of surface affects whether the point is easier for the server to win [20]. Another aspect that clearly influences performance in the sport is the rally length. Researchers have corroborated that most rallies end before five shots, which is more accentuated on fast surfaces [24,25]. This aspect of the game has a significant impact on match strategy [10,26]. Another determining factor is the moment of impact with the ball, which is influenced by position on the court when a stroke is made, the type of stroke played, shot direction and court position of the opponent [27]. For example, some studies suggest that match winners spend more time in the offensive zone than losers [28,29]. These aspects have been studied independently, but not through a combined analysis that could provide more effective information to improve training and subsequent performance in competition.

Based on the above, the main objective of this study is to determine the probability of winning a point in elite men’s singles tennis considering several variables that the literature has shown to be key to competitive performance in tennis. Data from the late rounds of three of four Grand Slam men’s singles tournaments allow the documentation of the tactical demands and success rates at the highest level of play of the sport.

In order to achieve this objective, the following hypotheses (H) will be taken into account:

H1. Most of the points are played on the first serve, with no difference between the surfaces, although there are more aces on fast surfaces.

H2. Short rallies predominate in men’s professional tennis today, although they are more common on fast surfaces.

H3. Most points are won when a player hits the ball in the offensive zone, when the ball hit by the opponent bounces in the service zone or in the middle zone of the court (in zone 1 or zone 2 according to our study).

H4. On fast courts, the player on serve wins a higher percentage of points than on clay.

H5. There are combinations of variables (patterns of play) that increase the probability of winning the point.

H5a. The first serve favours the probability of winning the point, being more accentuated on fast surfaces.

H5b. The second serve significantly reduces the probability of winning the point compared to the first serve.

H5c. The first serve—short rally combination is the most likely to win the point for the server.

H5d. Point ending varies by court type, service and rally length.

Method

Design

In order to approach the objectives of this research, observational methodology was used [30]. The observational design [31] used is nomothetic (all points played in the final rounds of the Grand Slams in 2021 -from the quarter-finals onwards-, excluding the Australian Open), follow-up (one season), and unidimensional (there is no concurrence of behaviours).

Sample

All points from men’s matches from the quarter-finals onwards of three of the four Grand Slam tournaments of the 2021 season were registered (one per type of court surface), seven matches per tournament (1660 points at Roland Garros, 1623 at Wimbledon and 1386 at the US Open). Seventeen different men’s tennis players were analysed. The study was approved by the Ethics Committee of the Faculty of Education and Sport Science (University of Vigo, application 02/0320).

Instruments

The observational instrument for this study was made ad hoc, although it was based on a previously validated observation instrument on match analysis in tennis, which had similar objectives to the present research [2]. The instrument described in Table 1 is a system of categories [30] called OBSTENNIS-S21 (Tennis observational instrument for the 2021 season). The validity of the construct of the observation instrument was done by its coherence with the theoretical framework [32] and by consulting two tennis and observational methodology experts who reached a degree of agreement of 95% in response to a questionnaire about the observation instrument, analysing the suitability of it for the reality of the competition and by following the same procedure as previous studies [2,33]. The two experts were provided with a comprehensive description of the observation instrument, the objects of the investigation and instructions for answering the questionnaire. The questionnaire consisted of five items (with a Likert scale of five levels) about its suitability to the object of study, compliance with the criteria of completeness and mutual exclusivity, clarity in the wording of the categories and the degree of objectivity that allows the data collection to be unified by various observers.

Table 1. OBSTENNIS observation instrument.

VARIABLE	CODE	DESCRIPTION
SERVICE	FS	The point is played with first serve.
	SS	The point is played with second serve.
	DF	The server makes a double fault.
RALLY LENGHT	SH	Short rally (0–4 shots).
	MD	Medium rally (5–8 shots).
	LN	Long rally (9+ shots).
BOUNCE ZONE	SZ	The point ends from the service zone (ace or double fault).
BOUNCE ZONE	ZB1 to ZB5	The zone of the court where the ball bounces before a winner or forced error (in this case the player who wins the point is registered). In the case of an unforced error, the bounce before the error is registered. In the case of a volley or smash, the area where the player’s feet are placed is registered.
THE FINISH ZONE	Z1 to Z5	Zone of the court where the ball is finally directed (only for winners and forced errors).
	NET	The final shot goes into the net or does not reach the net.
	LTO	The final shot goes out on the lateral side.
	BSO	The final shot goes out on the baseline.
WINNER	SW	The server wins the point.
WINNER	RE	The returner wins the point.
POINT ENDING	SWW	The server wins with a winner.
	SWFE	The server wins with a forced error.
	SWUE	The server wins with an unforced error.
	RWW	The returner wins with a winner.
	RWFE	The returner wins with a forced error.
	RWUE	The returner wins with an unforced error.
FINISH AND FINAL STROKE	SACE	The server wins with an ACE.
	SWFH	The server wins with a forehand winner.
	SWBH	The server wins with a backhand winner.
	SWOT	The server wins with a winner with another type of stroke (drop shot, smash, volley. . .).
	SFEFH	The server wins with a forehand by a forced error of the opponent.
	SFEBH	The server wins with a backhand by a forced error of the opponent.
	SFEOT	The server wins with another type of stroke by a forced error of the opponent.
	SUEFH	The server wins with a forehand by an unforced error of the opponent.
	SUEBH	The server wins with a backhand by an unforced error of the opponent.
	SUEOT	The server wins with another type of stroke by an unforced error of the opponent.
	RDF	The returner wins by double fault of the serving player
	RWFH	The returner wins with a forehand winner
	RWBH	The returner wins with a backhand winner
	RWOT	The returner wins with a winner with another type of stroke (drop shot, smash, volley. . .).
	RFEFH	The returner wins with a forehand by a forced error of the opponent.
	RFEBH	The returner wins with a backhand by a forced error of the opponent.
	RFEOT	The returner wins with another type of stroke by a forced error of the opponent.
	RUEFH	The returner wins with a forehand by an unforced error of the opponent.
	RUEBH	The returner wins with a backhand by an unforced error of the opponent.
	RUEOT	The returner wins with another type of stroke by an unforced error of the opponent.

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Note. A winner occurs when a player is unable to touch the ball with their racquet before it bounces twice during a match. A forced error is one where the player who commits the error is seen to have had a ball that was unreturnable. An unforced error is one where a player has a playable ball and commits a fault or hits the net with his return with no mitigating circumstances.

The OBSTENNIS-S21 (Table 1 and Fig 1) is made up of seven criteria and 50 categories. Data registration was performed with LINCE PLUS software [34].

Procedure

Data collection was carried out by recording the matches of three out of the four Grand Slams of the 2021 season (one on each surface). The videos were recorded at a resolution of 1080p (1920x1080). These matches were viewed for analysis on 27-inch monitors. Prior to the data quality testing, which was carried out with two experts in tennis and observational methodology, training in the use of the observation instrument was conducted. The training consisted of familiarisation with the observation tool. For this purpose, nine 2-hour sessions were held over three weeks using videos of men’s tennis matches from the 2020 season.

To ensure rigour in the registration process [35], the quality of the registered data was controlled by calculating intra- and inter-observer agreement using the Kappa coefficient [36] calculated using LINCE PLUS software. Both concordances were performed on points that did not belong to the final sample (n = 450; 1/10 of the final sample). The intra-observer kappa was 0.93 for the first observer and 0.96 for the second observer. The inter-observer kappa was 0.94. After the data quality tests, observer 2 carried out the analysis of all the points in the research sample.

After registering all of the points, we obtained an Excel file with the sequence of the actions that occurred at each of the points analysed. The versatility of this Excel file allowed us to automatically transfer the information to an SPSS file, the software with which the different statistical analyses of the research were carried out.

Data analysis

All statistical analyses were performed using IBM- Statistical Package for the Social Sciences, version 25.0 (IBM-SPSS Inc., Chicago, IL, USA). Statistical significance was assumed for p<0.05.

The χ2 test was used to contrast the differences between the categories of each criterion used (intra-criteria analysis), as well as to compare the differences between the playing surfaces (clay, grass or hard court) of Roland Garros, Wimbledon and US Open (inter-criteria analysis).

The analysis of the probability of winning a point as a function of the combination of performance indicators selected by the researchers was carried out in three steps: firstly, we segmented the file by groups according to the court surface; secondly, a selection of cases was made (based on the serve, rally and point ending) and finally a frequency analysis with the study variable "winner" (server or returner win). To determine whether there were significant differences between the winner variable and the court surface according to a previous selection of performance indicators (based on the serve and rally), a cross-table test was carried out using the chi-square statistic.

Results

Analysis of variables influencing performance in men’s singles tennis at Grand Slams

The information obtained from the different variables studied (Table 2) shows that, regardless of the court surface (clay, grass or hard), the majority of points started with the first serve (65.1–61.8–63.6% respectively), although there are no statistically significant differences depending on the type of court. The surface with the most aces was hard court (11.2%) and the least on clay (6%) (H1). Short rally points predominated on any type of court (64.9–77.4–68.8%) although they were more frequent on fast surfaces (H2).

Table 2. Description of the performance variables of the matches on the different court surfaces in the 2021 season and comparative analysis between court surfaces (χ2 inter-variable).

Study variables		Clay		Grass		Hard		χ2 Inter-variable
		n	%	n	%	n	%
SERVICE	DF	45	2,7	60	3,7	58	4,2	χ2 = 8,174
	FS	1081	65,1	1003	61,8	882	63,6	p = .085
	SS	534	32,2	506	34,5	446	32,2
RALLY LENGHT	LN	223	13,4	112	6,9	182	13,1	χ2 = 74,200
	MD	359	21,6	254	15,7	250	18,0	p = .000
	SH	1078	64,9	1257	77,4	954	68,8
BOUNCE ZONE	SZ	141	8,5	187	11,5	213	15,4	χ2 = 66,987
	ZB1	711	42,8	797	49,1	596	43,0	p = .000
	ZB2	441	26,6	339	20,9	313	22,6
	ZB3	197	11,9	186	11,5	164	11,8
	ZB4	99	6,0	56	3,5	57	4,1
	ZB5	71	4,3	58	3,6	43	3,1
THE FINISH ZONE	BSO	278	16,7	234	14,4	218	15,7	χ2 = 87,508
	LTO	229	13,8	155	9,6	121	8,7	p = .000
	NET	279	16,8	332	20,5	269	19,4
	Z1	400	24,1	534	32,9	460	33,2
	Z2	94	5,7	103	6,3	81	5,8
	Z3	56	3,4	42	2,6	34	2,5
	Z4	135	8,1	97	6,0	105	7,6
	Z5	189	11,4	126	7,8	98	7,1
WINNER	RW	615	37,0	565	34,8	489	35,3	χ2 = 1,972
WINNER	SW	1045	63,0	1058	65,2	897	64,7	p = .373
POINT ENDING	RWFE	89	5,4	62	3,8	58	4,2	χ2 = 26,960
	RWUE	393	23,7	402	24,8	334	24,1	p = .003
	RWW	133	8,0	101	6,2	97	7,0
	SWFE	246	14,8	335	20,6	252	18,2
	SWUE	392	23,6	358	22,1	314	22,7
	SWW	407	24,5	365	22,5	331	23,9
FINISH AND FINAL STROKE	RDF	38	2,3	60	3,7	58	4,2	χ2 = 172,046
	RFEBH	23	1,4	22	1,4	25	1,8	p = .000
	RFEFH	61	3,7	30	1,8	29	2,1
	RFEOT	5	,3	10	0,6	5	0,4
	RUEBH	133	8,0	77	4,7	105	7,6
	RUEFH	183	11,0	196	12,1	150	10,8
	RUEOT	40	2,4	69	4,3	20	1,4
	RWBH	28	1,7	24	1,5	26	1,9
	RWFH	73	4,4	42	2,6	50	3,6
	RWOT	31	1,9	35	2,2	21	1,5
	SACE	99	6,0	125	7,7	155	11,2
	SFEBH	92	5,5	152	9,4	101	7,3
	SFEFH	139	8,4	166	10,2	137	9,9
	SFEOT	16	1,0	17	1,0	14	1,0
	SUEBH	172	10,4	135	8,3	145	10,5
	SUEFH	193	11,6	181	11,2	157	11,3
	SUEOT	26	1,6	42	2,6	12	0,9
	SWBH	51	3,1	22	1,4	28	2,0
	SWFH	168	10,1	112	6,9	89	6,4
	SWOT	89	5,4	106	6,5	59	4,3

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Note. Abbreviations in Table 1.

Nearly half of the points analysed on the three surfaces ended after a bounce previous to the final shot into zone 1 (42.8–49.1–43%) (H3) and ended with a winner or forced error in zone 1 (24.1–32.9–33.2%). The points won (with a winner or forced error by the opponent) in zone 5 also stood out, especially on clay (11.4%). The surface where most points were won on the service was grass, compared to hard court and clay (65.2–64.7.8–63%). In any case, there are no statistically significant differences (H4).

With both, serving and returning, it is on clay where more winners were obtained (24.5% and 8% respectively; on grass 22.5% and 6.2% and on hard court 23.9% and 7%), although it is also the surface (serving) where fewer points were obtained by forced error (14.8% compared to 20.6% and 18.2%). An even amount of points won by returning (23.7–24.8–24.1%) and serving (23.6–22.1–22.7%) by unforced error was registered on all three surfaces. Unforced errors on the return had similar percentages after hitting with forehand and backhand; however, when serving, they were much more frequent with the forehand. Winners were more frequent with the forehand, both serving and returning, and were considerably more frequent when serving.

Statistically significant differences (p<0.05) were found between the categories of each of the variables and on each of the three surfaces analysed (intra-variable χ2). In the comparison between surfaces (inter-variable χ2 test), statistically significant differences were observed in the type of rally length (greater number of medium and long rallies on clay than on grass and hard court), the bounce zone (greater number of actions from zone 2 and fewer from the service on clay compared to the other surfaces), the finish zone (among other aspects, fewer finishings to the net and more shots were played to the lateral zone and to zone 5 on clay compared to other surfaces), the type of point ending (on fast courts more points were won on the serve due to a forced error by the opponent) and the type of the finishing (for more details see S1 Table).

Probability of winning a point as a function of different combinations of variables influencing performance

Fig 2 shows the server’s chance of winning the point on the different court surfaces analysed as a function of the following combination of variables: service and rally length (H5).

The probability of winning a point with the first serve was lower on clay than on grass or hard court (69-75-75% respectively) (H5a). The chance of winning the point with the second serve was very similar on all three surfaces (55-54-57%), which meant a decrease in the probability on all surfaces relative to the first serve (reduction of 14-21-18% respectively) (H5b). The first serve—short rally combination was the most successful for the server in terms of winning points (77-80-81%) (H5c).

Statistically significant differences were observed between court surfaces in the combination first service-long rally (χ2 = 8.052; sig. = 0.018). On both, grass and hard court, the returner was more successful in this type of combination, but not on clay.

Figs 3 and 4 show an analysis of point endings (winners, forced errors or unforced errors) on the different court surfaces based on the following combination of variables: service and rally length (H5d).

Significant differences between court surfaces were found in the first service−short rally combination (χ2 = 8.052; sig. = 0.018) and in the first service−long rally combination (χ2 = 26.106; sig. = 0.004). In the first of the aforementioned combination, a greater number of points ended by unforced errors were registered on clay than on the rest of the surfaces, both on the serve and on the return. Similarly, the number of forced errors (made by the returner) won when serving was higher on grass than on the other surfaces. In the second combination, the number of unforced errors in points won on the return was far higher on grass and hard than on clay. There were also more winners on clay than on grass and hard (in both serving and returning points).

Further statistical information of variables influencing performance

S2 Table shows the points observed as a function of the surface considering the following variables: service (first and second) rally length (short, medium and long), bounce zone (ZB1-ZB5), the finish zone (Z1-Z5 or BS, LT and NT) and point ending (server or returner won the point by forced error, unforced error or winner) (H5).

The most frequent combination based on the serve, rally length and bounce zone found was first serve, short rally and final shot after a bounce in zone 1, representing 25-35-30% of the total depending on the surface (clay, grass and hard court). In this situation, only 14% of the points were won on the return (the value is the same on all three surfaces). When serving, it is on clay where more points were won by winners (23%) and by unforced errors by the opponent (33%). The percentage of winners to zone 1 (41%) and zone 5 (28%) stands out in comparison with the others. Unforced errors are preceded by a shot that goes out over the baseline (zone 3–52%-). On grass, forced errors (48%) were more frequent and were mostly directed to zone 1 (88%). This was also the case with winners (52%), but those directed to zone 2 (15%) and zone 5 (16%) also represent a significant proportion of the total. The points won by serving through an unforced error by the opponent are usually sent to the net (59%). Most of the points won by the returner are by unforced error with a shot to the net (59%). On hard court, the data were similar to that of grass, with a high number of forced errors (46%) played to zone 1 (93%). Winners (16%) were mostly directed to zone 1 (44%) but there were shots played to all other zones (around 15%).

S3 Table shows an analysis of the points registered as a function of the surface considering the following performance indicators: service (first and second), rally length (short, medium and long), finish (forced error, unforced error and winner) and final stroke (backhand, forehand or other strokes).

On clay, winners, when the player served, were more frequent with the forehand than with the backhand in all the possible match situations studied, something that did not occur on grass or hard court. Returning, winners were predominantly with the forehand on clay and hard court, but on grass, many points were resolved with another type of stroke (probably volleys). Unforced error points won on the serve were very even on clay with first serves (regardless of the type of rally) and second serves in short rallies. On grass and hard courts, this balance was only evident with first serves in short and medium rallies. In all other cases, forehand errors predominated, except in short rallies in second serves on hard courts.

The points won by unforced errors on grass, when the player was returning, was linked to a forehand stroke (occurred in all possible solutions). On clay it was the same in short rallies (both first and second serves), there was a balance between forehand and backhand errors in medium rallies and in long rallies the error was preceded by a backhand stroke. On the hard surface, there was a balance of forehand and backhand errors in almost all the situations analysed, except for the first serve and short rallies, where the errors were clearly caused by a forehand stroke.

Discussion

This study identifies several patterns (combined information about different variables that influence performance) that determine the probability of winning a point in elite men’s singles tennis, which can help players and coaches define match strategy.

The results obtained on first serve accuracy (ball put into play) are similar to the results found in other research on elite men’s singles tennis [3,18] (58–64% on clay, 65% on grass and 63–65% on hard court), which confirms H1. It was also evident that aces were more common on hard surfaces (grass and hard court) than on clay [10,20], which supports H1. Most of the points, although there are differences between surfaces, were played with a short rally (64.9% on clay, 77.4% on grass and 68.8% on hard), results that confirm H2. The literature has contrasted that, for many years, the frequency of this type of rally has increased on all surfaces due to the increase in speed in tennis, even though for a period of time there was a slowdown due to the change in the type of balls (type III balls) [37]. This increase in speed is determined by various factors, such as improvements in racquet designs [38,39], greater physical ability or more aggressive playing strategies by players, where they put pressure on the opponent from the start [19,40,41].

As expected, most of the points are ended after a player hits a ball that bounces in the service zone or in the middle zone of the court (zone 1 or zone 2), an aspect that confirms the proposed H3. Several studies indicate that playing in the offensive zone of the court increases the chances of winning the match [42]. It has also been confirmed that the winners/forced errors are mainly directed to zone 1 (we assume that they are looking for angles that are difficult to return) and also to the opponent’s backhand zone (zone 5).

The data from this study does not fully confirm H4. Taking all points analysed, the server won 63% of all points on clay and 65% on grass and hard, so the differences are small. Previous studies have shown larger differences in the number of points won on different surfaces [20], which could be seen as evidence that tennis is evolving and that it is important to constantly analyse the variables that affect performance. The differences are clearer if we consider only points played with first serves (69% of points won on clay and 75% on grass and hard courts) or if we compare first serves combined with short rallies. Clay courts in Roland Garros induces slower and higher ball bounce, providing the receiver with the opportunity of returning more serves than on faster surfaces such as grass in Wimbledon [26].

The probability of winning a point when a point is initiated with a first serve was similar to that contemplated by other researchers in the same context [3,18,23] (69–75% on clay, 75–79% on grass and 70–78% on hard court). According to the researchers cited above, the chance of winning a point with a second serve fluctuates depending on the surface (between 51–56%, 53–58% and 48–55% respectively), values also close to that obtained, in our case, in the 2021 season. These results are consistent with the friction coefficient and coefficient of restitution data that place grass as the surface with the highest speed, ahead of hard court and clay [43]. It is confirmed that the first serve is fundamental to win matches in this sport, especially on fast surfaces [15,16,21,44] as the loss of efficiency with second serves is notable, being more accentuated on fast courts. Considering that playing second serves generates a probability of winning the point of around 55%, first serves might be an effective match strategy. Current data suggests that for elite men players, starting the point with a first serve instead of a second serve can increase the probability of winning the point by 14–21%, depending on the surface. This information corroborates H4a and H4b.

The study confirmed that the combination of first serve—short rally is the most likely to win the point for the server, something that has already been pointed out by several recent studies and which confirms H4c [15,45,46]. It has also been shown that players who dominated short rallies (0–4 shots) won the match in 9 out of 10 cases on both clay and grass [17,25], so tactical training should try to improve this aspect of the game.

The best strategy for the player that was serving with a first serve and short rally (server won between 77–81% of all points) was to achieve a winner, as 34–36% of the cases obtained the point later on either surface (taking into account the ending of all points). In addition, especially on grass, a high percentage of forced errors by the opponent (points won by the server) was registered (32%), dropping to 27% on hard and 20% on clay (data refers to all points). The best return strategy was to seek an unforced error from the opponent, regardless of the surface, although this was more effective on clay (17%) than on grass (15%) or hard court (13%). Return winners after the first serve were rather scarce in short rallies (3–4%). With this information, and considering that the returner is probably always at a tactical disadvantage [16], training to work on the defence of the serve should be aimed at trying to put the server in a situation of discomfort, rather than trying to find a winner, at least until after four shots. Hitting the ball to zone 3 (baseline) and zone 4 (backhand on the right-handed player) were the most effective, confirming that the search for the opponent’s error occurs more frequently on the backhand stroke [27].

Medium rally points represented 21.6–15.7–18.0% of all points in this study depending on the surface. According to one study [25], dominance on these points determines the winner of a clay court match by 65% and 69% on grass (approximately 25% less than dominance on short rallies). With the first serve, the tactic of looking for a winner on either surface was no more effective than seeking an unforced error from the opponent. It was the same for the returning player. The points won by unforced error were very balanced between server and returner, so it seems clear that, at a strategic level, the server has no interest in reaching this type of rally [45]. This highlights the importance of having an effective service to end the point as soon as possible and thus improve the probability of success at the point [15,16,45,47]. With the second serve, the statistics do not vary much with respect to the first serve. In fact, although on fast surfaces and medium rallies there was a decrease in the probability of winning the point between the first and second serve (from 55% to 51% on grass and from 52% to 46% on hard courts, on clay there was even an increase from 49% to 57%). This would suggest that the effect of the serve is diluted after the fifth shot, something that has already been suggested in the literature [45] and which again corroborates the H5c. On clay, with this rally and second serve, the tactic of looking for a winner (winner or forced error) was more effective than looking for the unforced error. In the return game, the opposite was true. On grass it was more effective to look for the unforced error both on the serve and on the return. On hard courts it was much more effective to look for the unforced error, especially in the return.

Long rally points were few in general, although this fact was more pronounced on grass, where not even 7% were observed. Although it was not investigated whether these points were important points (e.g.: breakpoints), the literature has pointed out that dominance on these types of points is linked to match success in 66% of cases on clay and 61% on grass [17], quite similar to that of medium rallies, so they must also have their importance in training, both from a physical, psychological and strategic point of view. In fact, there is a curiosity, at least on fast surfaces. The server won fewer points after a first serve than the returner, but significantly increased his effectiveness with a second serve. The explanation for this phenomenon is complex, but it may be possibly linked to aspects of concentration, due to starting from a disadvantageous situation (second serve). Although this data provides information on the probability of winning the point with a long rally, it would not make sense to make a recommendation to a player who is going to serve on the basis of the information provided.

Considering the information described in these last three paragraphs (each of which focused on one type of rally), and where different combinations of variables affecting the probability of winning the point were carried out, we can confirm H5 of the study, which supports that there are patterns of play that should be taken into account for improving match strategy in elite men’s singles tennis competition.

Conclusions

The first serve in elite men’s singles tennis is essential to increase the chance of winning the point on all types of rally and surfaces. Starting the point on the second serve decreases the chances of winning the point by 14.6% (clay), 21% (grass) or 17% (hard court) compared to the first serve (success rate: 69-75-75% respectively). The probability of winning the point with a first serve and medium rally decreases by 25–29% depending on the surface compared to finishing with a short rally (success rate: 77-80-81% respectively). On grass only 7% of points end with a long rally (13% on the other two surfaces). The most common point winning combination of shot for the server was the one in which the point started with a first serve, there was a rally of less than five shots and the point ended after a bounce of the ball in the service zone (zone 1) and a subsequent aggressive shot. This generated an unforced error in the opponent (on clay) or a forced error (on grass and hard court).

Practical application

Throughout this article, although focusing on service, surface and rally length, different information has been established for the probability of winning a point depending on different variables. The stroke patterns and success/errors rates are what are currently present at the highest levels. Male players seeking this level of play should consider training and shots in match play that align with greater probability of success. We recommend that coaches make use of the different tables and figures in the manuscript, as well as the supplementary material to obtain more specific data, where an analysis is also made according to the bounce zone prior to the last shot, type of final stroke and direction of the final shot, all differentiated by surface, type of service and rally length.

Limitations and future perspectives

In this study we have only analysed matches from the Grand Slam quarter-finals onwards, so previous rounds have not been taken into account. Therefore, only a small number of players were analysed who, in addition, could be under the effects of psychological stress and physical fatigue. The stroke patterns and error rates observed may not be similar for players from other sex, skill level, or disabilities. The direction of the serve (wide, body or T zone) and its speed have not been considered, aspects that could be interesting to address in future research.

Bearing in mind that most of the points end in short rallies (0–4 shots), in the future studies should be carried out linked to the exact number of shots in the point, focusing on this type of rally, in order to establish more specific point winning probabilities, taking into account all the movements and strokes made by the players.

Supporting information

S1 Table. Description of variables that affect performance on the different court surfaces in the 2021 season, analysis of the distribution of categories of each variable by surface (intra-variable χ2) and comparative analysis between court surfaces (χ2 inter-variable).

(DOCX)

Click here for additional data file.^{(36.2KB, docx)}

S2 Table. Analysis of different combinations of variables that affect performance (service, rally length, bounce zone, the finish zone and point ending) as a function of the court surface.

(DOCX)

Click here for additional data file.^{(86.9KB, docx)}

S3 Table. Analysis of different combinations of variables that affect performance (service, rally length, final stroke and point ending) as a function of the court surface.

(DOCX)

Click here for additional data file.^{(38.6KB, docx)}

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

This study was funded by the Ministerio de Cultura y Deporte (https://www.culturaydeporte.gob.es/portada.html), Consejo Superior de Deportes (https://www.csd.gob.es/es) and European Union (https://european-union.europa.eu/index_es) under Project “Integración entre datos observacionales y datos provenientes de sensores externos: Evolución del software LINCE PLUS y desarrollo de la aplicación móvil para la optimización del deporte y la actividad física beneficiosa para la salud (2023)” EXP_74847 to IPL and AGS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0286076.r001

Decision Letter 0

Javier Abián-Vicén

23 Jan 2023

PONE-D-22-33800Analysis of effectiveness through patterns of play in professional men's tennisPLOS ONE

Dear Dr. Prieto-Lage,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Reviewer #2: Partly

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Reviewer #1: Initial comments

The reviewer would like to congratulate the authors for conducting research in tennis. More specifically, for studying the strategy of the game at the high-performance level.

The purpose of this review is to share with the authors several comments that will help to improve the quality of the manuscript since, in the views of the reviewer, there are some relevant aspects that need to be clarified, changed, referenced and deleted.

To assist in the review, the reviewer has enclosed the original pdf of the manuscript with his suggestions and comments. By doing this, the authors will find it easier to follow the recommendations.

Structure of the paper

No comments regarding the structure of the manuscript.

Language

The reviewer suggests that the language is double checked by an English native expert. More importantly, it has to be someone that is familiar with the game of tennis, since some of the words and constructions used in the text are not completely accurate.

Introduction

The authors mention the concept of patterns of play but this concept is not defined in the manuscript. Throughout the document there seems to be a confusion between shots, patterns, behaviours, sequences, etc. This has to be clarified by the authors using the adequate references.

The authors state the objective of the paper but they do not include any hypothesis that they would like to test. This is a very important aspect that also needs to be addressed. Hypotheses have to be included and then, the results and the discussion should refer to them.

Methodology

The authors use an observational methodology based on an instrument that it is not indicated if it has been used in previous research nor if it has been validated. Again, this has to be clarified to ensure that the tool used is adequate for the purposes of the study.

Furthermore, they use a division of the tennis court in zones that, again, it is not indicated if it has been used in previous studies, nor if it has been validated. The reviewer is not sure where they have taken the references to divide the court in those zones.

Finally, with the technological advances available nowadays, the notational analysis is mostly conducted using other types of methodologies provided by the statistics and computer programmes available.

Results and discussion

The reviewer feels that the results should be structured based around the hypotheses set. If this is not the case, they will not be clear enough.

Conclusion

They seem adequate for the goal of the paper. However, the reviewer feels that the limitations and the practical applications should be part of the conclusion and not be included in the discussion.

Final recommendation

Due to the comments made above, the reviewer feels that the manuscript needs some major changes that will substantially improve its quality.

Finally, the reviewer would like to thank again the authors for their passion and effort in producing this manuscript in tennis.

Miguel Crespo, PhD.

Reviewer #2: The report provides a retrospective analysis of categorical data of elite male tennis players in the final rounds of the most prestigious tournaments. The report suffers from unclear writing that makes it difficult for even motivated, tennis-interested readers to follow. The specific comments to the authors outline weaknesses that should be addressed.

Page-Line

2-4 . . . understanding of tactical actions in tennis. In particular, sequential analysis is needed to understand the relationships between actions in competitions.

2-8 Add wording that all matches at these tournaments were analyzed and if these represent males, females, or both

2-10 Clarify wording if these are means and what is “effectiveness?” Ball put in play or point won?

2-14 . . . won by the serving player? Medium rallies or longer?

2-17 Revise. You have no prospective evidence of tennis training. You have descriptive data of typical patterns of play in elite tennis players. This only informs what are typical and likely effective tactics and could inform coach and player judgments on training and match strategy

3-3 Revise this incorrect sentence. Like many sports, tennis has a long history of qualitative and quantitative performance analysis—most predating the automatic data collection systems seen recently in modern sport. This point is well presented in the Reid et al (5) article you cite, although they ignore classic texts in tennis (Classic textbooks by Talbert & Old, 1956, 1962, 1968, 1977 on singles and double tennis), early but influential tennis notation systems like COMPUTENNIS (Daw & Burton 1994; Kahn et al. 2004) and iCODA (O’Donoghue 2014; Wright et al. 2012). Your paper also cites quite a few more recent studies of notational data on tennis. The key observation here is that automated data collection and reporting systems has just accelerated recently because of these tools and growing sport/coaching awareness of the value of analytics. The first issue of the Journal of Sports Analytics was only 2015 and Int J Perform Analy Sport in 2001

3-11 to 13 Revise to clarify. Last sentence is good but next to last is an exaggeration/hyperbole. It is rare that any set of numbers can “optimize” training or performance. Even if such a complex system in a complex sport could be somewhat likely enhanced, in professional tennis there are limits on what a coach can communicate with players during a match

3-14 Revise this hyperbole: Delete implied and unobtainable optimality and focus on the search for meaningful variables for performance enhancement

3-22 Please stop with the meaningless hyperbole and one-sentence paragraph? What is optimal results in tennis? Winning every point in the match? Clearly communicate previous results and their meaning. How can a reader believe you that which stroke in tennis in any situation can be perfectly predicted, although it depends on many other things, and “in any case” the sport is just about the serve?!

4-10 Vague wording. Different tournaments have different surfaces AND natural surfaces (clay & grass) have within tournament variation over time

4-13 Revise for clarity. You are referring to the position on the court when a stroke is made . . . .shot direction and court position of the opponent

4-18 Reword for clarity. Do you mean the chance of winning a point in a tennis match based on what or number of performance indicators?

5-6 Analysis of only final rounds does not agree with wording in the abstract

5-11 Again, note if these are matches with males, females, or both. Also report the sample size since some players win and compete again, as well as may be in several tournament late rounds

6-Table 1 Correct tennis terminology (e.g., double fault). Perhaps have a table note with operational definitions of common tennis terms that may not be obvious to readers (e.g., forced versus unforced error)

7-4 Describe extent of training. Size and resolution of video, television used, and ability of observers to replay video sequences

7-11 Vague. Please expand on what transformations were made and how this relates to the results of this previous study. Why are the results from reference 27 not summarized in the introduction (page 3)?

7-17 It is unclear how your rationale of a sequential analysis for this study aligns with your comparative analysis of tennis performance categorical data. Why is p < 0.05 is appropriate for this study given you have numerous Chi squared tests of likely correlated dependent variables. The family-wise type I error rate for this study is quite large, so you likely have some effects falsely identified as significant and you don’t know which ones are. An effective and easy approach is the progressive Holm (1979) correction

7-last line Describe this technique or cite a reference so readers can replicate your study

8-3 Good scientific reporting will have a narrative describing key results and parenthetically refer to the numerous specifics in a table. Combine this revised sentence with your results paragraphs

9-3 Is this correct? Inter-criterion for service was p = 0.085 and winners p = 0.373

9-11 This paragraph mixes vague observations of nonsignificant and statistically significant

10-9 Correct same error as line 8-3. Describe results and only parenthetically refer details in figure. Figure 2 needs a clear caption to facilitate reader understanding. This is also hard to understand given the vague presentation of what is meant by “effectiveness” and “sequential.” What is RG, WI, US?

11-10 through page 12 Consider condensing with only general results for interested readers to explore. The problem with you data are that they are likely biased by the handedness and players in these late rounds. It is hard to make sense of all these trends and what they might mean given the interaction with other factors (player, environment, match situation, handedness, etc)

13-4 Readers should not be finding out in the discussion that the data only refer to male professional players

13-6 Do not repeat your reporting of means by surface. The report improved by integrating this idea with your reporting of previous values

13-19 . . . first serves might be an effective match strategy. The current data indicate that this might increase points early between 14 and 21% for elite players depending on the surface

13- last line Do not refer just the last few years. The speed of tennis has been increasing for many years due to improvements in racket design (Haake & Brody 1999; Haake et al. 2000, 2007) and was even considered slowing by changes (type III ball)

14-5 Unclear. What does it mean to look for a winner?

14-8 What does it mean to look for an unforced error?

16-5 Add additional limitation of small number of players and potential interaction on fatigue/stress of additional rounds of winners that are apparently treated as independent matches/observations and are not. Weakness of inflation of type I errors also needs to be noted if not controlled

Conclusions: Nice summary. Perhaps reword to chance of winning a point rather than “effectiveness” and other wording for a “final shot” (aggressive/challenging second shot)

**********

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Reviewer #1: Yes: Miguel Crespo

Reviewer #2: No

**********

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PLoS One. 2023 Sep 28;18(9):e0286076. doi: 10.1371/journal.pone.0286076.r002

Author response to Decision Letter 0

8 Mar 2023

A document with the response to the reviewers is attached.

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PLoS One. doi: 10.1371/journal.pone.0286076.r003

Decision Letter 1

Javier Abián-Vicén

30 Mar 2023

PONE-D-22-33800R1Match analysis and probability of winning a point in men's tennisPLOS ONE

Dear Dr. Prieto-Lage,

==============================

ACADEMIC EDITOR:I have completed my evaluation of your manuscript. I would like to thank the authors for their efforts on this second version of the manuscript. The reviewers recommend reconsideration of your manuscript following major revision. I invite you to resubmit your manuscript after addressing the reviewers' comments.

==============================

Please submit your revised manuscript by May 14 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

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We look forward to receiving your revised manuscript.

Kind regards,

Javier Abián-Vicén, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments:

I have completed my evaluation of your manuscript. I would like to thank the authors for their efforts on this second version of the manuscript. The reviewers recommend reconsideration of your manuscript following major revision. I invite you to resubmit your manuscript after addressing the reviewers' comments.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

Reviewer #2: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: Match analysis and probability of winning a point in men's tennis.

2nd review

Reviewer: Miguel Crespo, International Tennis Federation

Introduction

The reviewer would like to thank the authors for their efforts on this second version of the manuscript. In the reviewer's opinion, so many changes have been made that it really does look like a new document.

In addition, the reviewer acknowledges that the authors have considered many of the suggestions made in the first revision.

Similarly, the reviewer has noted that the authors have also taken into account many of the suggestions made by the second reviewer.

It is also noted that the authors have made considerable improvements in the English language, which makes the manuscript easier to understand.

However, despite the improvements made and the fact that the changes are so substantial that a new text could be considered, this reviewer considers that, without detracting from the interest of the work, there are still aspects that could be clarified and improved.

Therefore, in this second revision, the reviewer proposes to continue the academic debate raised by this new version in order to improve the manuscript as much as possible.

To this end, the following paragraphs are intended to assist in the improvement and development of the text presented.

Abstract

The authors have changed the abstract considerably but, in the reviewer's opinion, there are still some aspects that are unclear and contribute to a general confusion that does little to aid understanding of the text.

For example, they start by talking about tactical actions in tennis and then include the term technical-tactical performance factors. They then discuss the most important performance indicators in the sport. Obviously, the mention of three very similar terms, all related to each other, does not help to clearly understand what the authors intend. It seems as if they want to follow the well-known phrase: "If you can't convince them, confuse them".

And that is what happens to this reviewer with this new version in general. A constant confusion that, added to the lack of objectives and hypotheses, produces a sense of unfamiliarity and general lack of precision that does not help the manuscript.

In some sentences, such as "The end of the point (winner, forced error and unforced error) varies according to the court surface, the type of serve and the length of the rally" the obviousness is clear and probably does not need to be included.

Moreover, the final sentence is so ambiguous and general that it needs to be more specific to give practical value to the results of the study.

Introduction - KPIs

The changes made in the first part of the introduction, some of them suggested by the other reviewer, are appropriate and improve the quality of the previous version.

However, having reread this introduction and, in particular, the second part where the authors should go into detail about the research carried out on the subject of their study, this reviewer notes a lack of depth in the reference to the results of this research.

And this is where the reviewer notes a general confusion regarding the concept of key performance indicator. In this case, it seems that the authors consider serve and court surface to be key performance indicators in the same way as rally duration.

With all due respect, this reviewer believes that this is where the major confusion in this study lies. The serve, like the rest, the forehand or any other stroke, are technical actions or gestures. They can in no way be considered key performance indicators.

Similarly, the court surface, as well as the height of the court (at sea level or at altitude), or whether it is an indoor or outdoor court, or even the type of balls with which the match is played, cannot be considered as key performance indicators.

The fact that the authors have eliminated the different terms used to refer to what they call study variables (patterns, combinations of blows, behaviours, etc.) does not mean that the terminological confusion is not present throughout the text and should therefore be clarified and suitably modified to prevent the study, its approach, results and conclusions from being unclear and imprecise.

Similarly, when they refer to combinations of strokes (the so-called patterns), as in the case of the serve + a stroke, for example, they are not key performance indicators either, but rather patterns of play (obviously tactical) made up of combinations of gestures, but not KPIs.

Objective and hypotheses

With regard to the lack of hypotheses in the study, the authors justify this fact by the type of study in question. However, once again, in the opinion of this reviewer, if the authors do not wish to include hypotheses, which is regrettable considering the large number of previously conducted studies to which they could refer and from which to obtain possible hypotheses to validate in their research, they should at least include more objectives that would help them to structure the results and discussion section much better.

In this sense, the reviewer considers, once again, that, lacking clear, specific hypotheses based on the results of previous studies, which are not taken as a basis for this observational study, the research we reviewed lacks reference points as only one objective is established.

Again, with respect, the reviewer feels that in light of the results of previous research, the authors should set many more objectives to inform their study. In fact, a mention to the results of these previous studies should be included in the last section of the introduction to provide the necessary background to the study and to show that the authors have done the adequate homework in their literature study by extracting the main results of these previous works.

As they indicate in the new version “Based on the above, the objectives of the research were to analyse the technical-tactical performance indicators of men's Grand Slam tennis matches, as well as to study the probability of winning a point”, if these are the two objectives of the study, they should be stated more specifically based on the results of research previously obtained.

Methodology – instruments

It is important to note that the instrument used is, as stated by the authors, a system of categories. Again, this is a new term. Does this mean that categories are key performance indicators? Are they game situations? Are they patterns? Are they shots? The confusion continues and it does not seem to be clarified.

Regarding the validation of the instrument, the reviewer is unsure if the feedback of two tennis experts is enough to validate such a tool.

Furthermore, in the table 1 the confusion continues because the categories are then called “criteria”… thus not helping to clarify the overall confusion of terminologies.

Results

Again, when the authors state that they are analyzing technical-tactical performance indicators, there are several major issues: as indicated, the service is not a performance indicator, in the same way that the zone of the court in which the ball bounces, or where the ball is played to…

The titles, such as the probability of winning a point based on different combinations of performance indicators is again misleading, because the serve is not a performance indicator, nor the court surface, or zone of ball bounce…

Discussion

As indicated above, the results of these previous studies should be used as the main base to generate not only the structure of the results but also that of the discussion. This would help to provide the necessary clarity to the study and to show that the authors have done the adequate homework in their literature study by extracting the main results of these previous works and, once they have their own results, discussing adequately with those of their colleagues.

Conclusion

Again, the authors refer to the service, surface, and rally length. Two of these aspects are not key performance indicators, and this should be clarified.

The practical application section is extremely short, quite poor in content and suggestions for practitioners and with an obvious lack of belief in the applicability of the results obtained. It would be convenient to cover this with more depth and with a clear intention of sharing the positive aspects of the study.

Final words

Again, as in the first review, the reviewer would like to thank the authors for their interest in conducting studies on tennis. The reviewer hopes that the comments made in this review on the second version of the manuscript will be useful to the authors.

The fact that so many changes have been made has, on the one hand, helped to improve the English. However, on the other hand, new content, expressions, and terms have been introduced which may have contributed to confusion and opacity of the text.

The reviewer hopes that the authors receive these comments on the second version of the text as an attempt to improve the quality of the text and as part of the process involved in any scientific publication.

Once again, the reviewer's intention is to help the manuscript improve its quality and reach the appropriate level to be published in the journal. At the moment, this reviewer's recommendation is that the authors make considerable improvements to it.

Best regards,

Miguel Crespo, PhD.

International tennis Federation

Reviewer #2: The authors did a generally good job addressing the points of the initial review and the data on elite male players in singles tennis are valuable. They did not cite all the recommended articles or the older COMPUTENNIS data/Talbert & Old texts on patterns of tennis play. There is a need for clarity in focus on elite males singles (I presume singles rather than both singles and doubles) and reporting of “percentage points” or actual percentage differences. The revision still does not clearly communicate that the patterns and error rates are specific only to elite, male tennis players in singles match play. Please consider the revisions noted in the specific comments that follow.

Page-Line

2-1 . . . in elite men’s singles tennis

2-9

2-20 . . . on match strategy at the highest levels of elite men’s single tennis.

4-63 There should not be any one-sentence paragraphs. Consider revising to have a sentence with both objectives of tactical performance indicators and probability of winning at point in elite-level men’s tennis followed by a statement of importance. Something like: Data from the late rounds of three of four Grand Slam men’s singles tournaments allow the documentation of the tactical demands and success rates at the highest level of play of the sport.

14-245 to 311 Please revise carefully to clarify when you are talking about differences in error rates/percentages. For example, the wording should be: . . . “the probability of winning a point decreases 25 to 29 percentage points” if you are just subtracting error rates. When you calculate a percentage it is essential to know what the denominator is for the percentage calculation. So if you do not define this and just are subtracting percentages in different conditions, you have to clearly state that there is X percentage point difference, NOT at some percentage difference using some unspecified comparison condition

14-245 to 248 Delete redundant paragraph repeating the purpose of your study. Lead your discussion off with the most important observation(s) and their size/strategic meaning. Again, the author(s) should note that the results represent the highest level of performance in the sport—near and earning championship at the men’s elite leve1

14-249 to 253 Good comparison, but can there be more context added? Are previous studies of advanced or elite levels and both sexes?

17-315 . . . to the first serve in elite men’s singles tennis. Other option, perhaps better option, is to set up these conclusions from your data that relate to elite men’s singles tennis play.

17-318 . . . most common point winning combination of shot for the server was . . .

17-324 Revise to eliminate “guidelines.” You have general benchmarks for elite-level men’s tennis. You have no idea how different skill levels, disabilities, or sex of player might influence the pattens of play and error rates

17-329 How should coaches use this? Just say that these patterns and success/errors rates are what are currently present at the highest levels. Male players seeking this level of play should consider training and shots in match play that align with greater probability of success

18-344 Add a sentence that the stroke patterns and error rates observed may not be similar for players from other sex, skill level, or disabilities

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Miguel Crespo

Reviewer #2: No

**********

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PLoS One. 2023 Sep 28;18(9):e0286076. doi: 10.1371/journal.pone.0286076.r004

Author response to Decision Letter 1

1 May 2023

Please see attached document

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PLoS One. doi: 10.1371/journal.pone.0286076.r005

Decision Letter 2

Javier Abián-Vicén

9 May 2023

Match analysis and probability of winning a point in elite men's singles tennis

PONE-D-22-33800R2

Dear Dr. Prieto-Lage,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

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Kind regards,

Javier Abián-Vicén, Ph.D.

Academic Editor

PLOS ONE

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: The reviewer would like to thank the authors for considering most of the comments made. They have adequately addressed all the suggestions made. Finally, the reviewer would like to congratulate the authors for the considerable improvement of the manuscript.

Reviewer #2: Most all critiques have been addressed but one. Twice the authors have ignored the prompt to clearly point out that the relatively recent trust to automate, report, and analyze tennis tactical data is NOT the first attempts at gathering this knowledge. I know that PLOS One does not emphasize the need to provide clear justification/contribution for studies, but published reports should not OMIT or be dishonest about the novelty of ideas and scholarship. I find it frustrating that the editors keep requesting revisions but the authors to not, AT LEAST, note that the current data are just more recent efforts that follow initial work in the 1960s-70s (Talbert & Old) that were expanded by use of personal computers (COMPUTENNIS). A quick search for the latter turns of over a half dozen studies of tennis performance results using COMPUTENNIS.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Miguel Crespo

Reviewer #2: No

**********

PLoS One. doi: 10.1371/journal.pone.0286076.r006

Acceptance letter

Javier Abián-Vicén

16 May 2023

PONE-D-22-33800R2

Match analysis and probability of winning a point in elite men's singles tennis

Dear Dr. Prieto-Lage:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

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PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

(DOCX)

Click here for additional data file.^{(36.2KB, docx)}

S2 Table. Analysis of different combinations of variables that affect performance (service, rally length, bounce zone, the finish zone and point ending) as a function of the court surface.

(DOCX)

Click here for additional data file.^{(86.9KB, docx)}

S3 Table. Analysis of different combinations of variables that affect performance (service, rally length, final stroke and point ending) as a function of the court surface.

(DOCX)

Click here for additional data file.^{(38.6KB, docx)}

Attachment

Submitted filename: PONE-D-22-33800_reviewer MC notes.pdf

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Submitted filename: 2023 - Match analysis and probability - Crespo 2nd review.docx

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Data Availability Statement

All relevant data are within the paper and its Supporting Information files.

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PERMALINK

Match analysis and probability of winning a point in elite men’s singles tennis

Iván Prieto-Lage

Adrián Paramés-González

Daniel Torres-Santos

Juan Carlos Argibay-González

Xoana Reguera-López-de-la-Osa

Alfonso Gutiérrez-Santiago

Roles

Abstract

Introduction

Method

Design

Sample

Instruments

Table 1. OBSTENNIS observation instrument.

Fig 1. Court zones.

Procedure

Data analysis

Results

Analysis of variables influencing performance in men’s singles tennis at Grand Slams

Table 2. Description of the performance variables of the matches on the different court surfaces in the 2021 season and comparative analysis between court surfaces (χ2 inter-variable).

Probability of winning a point as a function of different combinations of variables influencing performance

Fig 2. Probability of winning the point on each surface as a function of type of service and rally length.

Fig 3. Point ending with first service.

Fig 4. Point ending with second service.

Further statistical information of variables influencing performance

Discussion

Conclusions

Practical application

Limitations and future perspectives

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Javier Abián-Vicén

Roles

Author response to Decision Letter 0

Decision Letter 1

Javier Abián-Vicén

Roles

Author response to Decision Letter 1

Decision Letter 2

Javier Abián-Vicén

Roles

Acceptance letter

Javier Abián-Vicén

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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